SynthesisThe European journal of health economics : HEPAC : health economics in prevention and care2019
How to make value-based health insurance designs more effective? A systematic review and meta-analysis.
Synthesis in The European journal of health economics : HEPAC : health economics in prevention and care, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The VBHC big picture: an umbrella review.BMC health services research · 2025Pooled it
- Pooled it
- The cost-effectiveness of value-based insurance design initiatives in managing cardiometabolic diseases: a systematic review.The European journal of health economics : HEPAC : health economics in prevention and care · 2026Article
- AMCP Market Insights: Impact of the evolving understanding of multiple sclerosis and emerging treatment approaches on managed care.Journal of managed care & specialty pharmacy · 2025Article
- A Chinese conundrum: does higher insurance coverage for hospitalization reduce financial protection for the patients who most need it?Health policy and planning · 2025Article
- The Relationship between Health Insurance and Pharmaceutical Innovation: An Empirical Study Based on Meta-Analysis.Healthcare (Basel, Switzerland) · 2023Article
- Association of Participation in a Value-Based Insurance Design Program With Health Care Spending and Utilization.JAMA network open · 2023Article
- Treatment Adherence in Integrative Medicine-Part One: Review of Literature.Integrative medicine (Encinitas, Calif.) · 2021Review
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Value-based health insurance designs (VBIDs) are one approach to increase adherence to highly effective medications and simultaneously contain rising health care costs. The objective of this systematic review was to identify VBID effects on adherence and incentive designs within these programs that were associated with higher effects. Eight economic and medical databases were searched for literature. Random effects meta-analyses and mixed effects meta-regressions were used to synthesize VBID effects on adherence. Thirteen references with evaluation studies, including 12 patient populations with 79 outcomes, were used for primary meta-analyses. For qualitative review and sensitivity analyses, up to 19 references including 20 populations with 119 outcomes were used. Evidence of synthesized effects was good, because references with high risk of bias were excluded. VBIDs significantly increased adherence in all indication areas. Highest effects were found in medications indicated in heart diseases (4.05%-points, p < 0.0001). Each additional year increased effects by 0.15%-points (p < 0.01). VBIDs with education were more effective than without education, but the difference was not significant. Effects of VBIDs with full coverage were more than twice as high as effects of VBID without that option (4.52 vs 1.81%-points, p < 0.05). These findings were robust in most sensitivity analyses. It is concluded that VBID implementation should be encouraged, especially for patients with heart diseases, and that full coverage was associated with higher effects. This review may provide insight for policy-makers into how to make VBIDs more effective.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.